Raw data may be difficult for humans to interpret. To ease interpretation, the data may be presented to users visually or graphically. For example, when data are associated with geographical locations, the data may be presented on a map, so that a user may easily associate the data with their associated locations.
When data are associated with multiple locations, the display may become cluttered when multiple data points are displayed, rendering interpretation of the data difficult. To reduce the cluttering, the data may be aggregated into clusters, whereby data from nearby locations on the map are aggregated into one location on the map. However, this aggregation of data from nearby locations may result in loss of information regarding data associated with locations which are geographically close to each other, but have significant differences, such as different regulatory environments caused by locations within different political jurisdictions.